macro-regime-detector

Name: macro-regime-detector
Author: mphinance

github.com/mphinanceData & databases3284 installsUpdated 5d ago

About macro-regime-detector

macro-regime-detector is a skill for investors assessing structural market shifts over a 1–2 year horizon. It analyzes monthly cross-asset ratios and Treasury rates, then produces a report with regime evidence and portfolio posture recommendations.

Key features

01Six weighted cross-asset and rate indicators
02Five regime classifications
03Monthly 6- and 12-month averages
04Markdown report and JSON output
05Historical regime reference material

Use cases

01Assess whether a structural regime change is underway
02Review concentration versus broader market participation
03Inform long-term positioning using rates and credit signals

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mphinance/alpha-skills --skill macro-regime-detector

After installing, type/macro-regime-detectorin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

README.md

references/

historical_regimes.md

indicator_interpretation_guide.md

regime_detection_methodology.md

scripts/

fmp_client.py

macro_regime_detector.py

report_generator.py

scorer.py

scripts/calculators/

__init__.py

concentration_calculator.py

credit_conditions_calculator.py

equity_bond_calculator.py

sector_rotation_calculator.py

size_factor_calculator.py

utils.py

yield_curve_calculator.py

scripts/tests/

conftest.py

test_concentration.py

test_credit_conditions.py

test_equity_bond.py

test_fmp_client.py

test_helpers.py

test_main_cli.py

test_report_generator.py

test_scorer.py

test_sector_rotation.py

test_size_factor.py

test_utils.py

test_yield_curve.py

Details

From the project README

Macro Regime Detector

Detect structural macro regime transitions using monthly-frequency cross-asset ratio analysis. This skill identifies 1-2 year regime shifts that inform strategic portfolio positioning.

When to Use

User asks about current macro regime or regime transitions
User wants to understand structural market rotations (concentration vs broadening)
User asks about long-term positioning based on yield curve, credit, or cross-asset signals
User references RSP/SPY ratio, IWM/SPY, HYG/LQD, or other cross-asset ratios
User wants to assess whether a regime change is underway

Workflow

Load reference documents for methodology context:
- references/regime_detection_methodology.md
- references/indicator_interpretation_guide.md
Execute the main analysis script:
```
python3 skills/macro-regime-detector/scripts/macro_regime_detector.py
```
This fetches 600 days of data for 9 ETFs + Treasury rates (10 API calls total).
Read the generated Markdown report and present findings to user.
Provide additional context using references/historical_regimes.md when user asks about historical parallels.

Prerequisites

FMP API Key (required): Set FMP_API_KEY environment variable or pass --api-key
Free tier (250 calls/day) is sufficient (script uses ~10 calls)

6 Components

#	Component	Ratio/Data	Weight	What It Detects
1	Market Concentration	RSP/SPY	25%	Mega-cap concentration vs market broadening
2	Yield Curve	10Y-2Y spread	20%	Interest rate cycle transitions
3	Credit Conditions	HYG/LQD	15%	Credit cycle risk appetite
4	Size Factor	IWM/SPY	15%	Small vs large cap rotation
5	Equity-Bond	SPY/TLT + correlation	15%	Stock-bond relationship regime
6	Sector Rotation	XLY/XLP	10%	Cyclical vs defensive appetite

5 Regime Classifications

Concentration: Mega-cap leadership, narrow market
Broadening: Expanding participation, small-cap/value rotation
Contraction: Credit tightening, defensive rotation, risk-off
Inflationary: Positive stock-bond correlation, traditional hedging fails
Transitional: Multiple signals but unclear pattern

Output

macro_regime_YYYY-MM-DD_HHMMSS.json — Structured data for programmatic use
macro_regime_YYYY-MM-DD_HHMMSS.md — Human-readable report with:
1. Current Regime Assessment
2. Transition Signal Dashboard
3. Component Details
4. Regime Classification Evidence
5. Portfolio Posture Recommendations

Relationship to Other Skills

Aspect	Macro Regime Detector	Market Top Detector	Market Breadth Analyzer
Time Horizon	1-2 years (structural)	2-8 weeks (tactical)	Current snapshot
Data Granularity	Monthly (6M/12M SMA)	Daily (25 business days)	Daily CSV
Detection Target	Regime transitions	10-20% corrections	Breadth health score
API Calls	~10	~33	0 (Free CSV)

Script Arguments

python3 macro_regime_detector.py [options]

Options:
  --api-key KEY       FMP API key (default: $FMP_API_KEY)
  --output-dir DIR    Output directory (default: current directory)
  --days N            Days of history to fetch (default: 600)

Resources

references/regime_detection_methodology.md — Detection methodology and signal interpretation
references/indicator_interpretation_guide.md — Guide for interpreting cross-asset ratios
references/historical_regimes.md — Historical regime examples for context

Comments

Markdown supported

Details

Author: mphinance
Repository: mphinance/alpha-skills
Category: Data & databases
Installs: 84
GitHub stars: 32

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